JP4634969B2 - Linear prediction model order determination apparatus, linear prediction model order determination method, program thereof, and recording medium - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To determine an optimal model degree of a linear prediction model without actually determining a code amount for each model degree. <P>SOLUTION: A PARCOR coefficient for each linear prediction model to a model degree (m) and a forward prediction error sequence and/or a backward prediction error sequence in the model degree (m) are calculated, respectively from an input signal. A code amount of a prediction error waveform that is an error between a predictive value of the linear prediction model of the model degree (m) and the input signal, is then estimated from the forward prediction error sequence and/or the backward prediction error sequence in the model degree (m). An individual code amount corresponding to respective PARCOR coefficients to the model degree (m) or an entire code amount corresponding to all the PARCOR coefficients is calculated. Regarding a plurality of model degrees, a total code amount of a total of entire code amounts in the respective model degrees (m) or of individual code amounts to the model degree (m) and estimated code amounts of the prediction error waveform, is found and one model degree is determined from the total code amount. <P>COPYRIGHT: (C)2008,JPO&amp;INPIT

Description

  The present invention relates to a linear prediction analysis technique used for signal analysis and signal coding.

The linear prediction model includes a sample value x _t of a time-series discrete signal at a certain time point {t}, p time points {t-1} past the time point {t}, a time point {t-2},. - the sample value _{x t-1} of the time-series discrete signal at the time {t-p}, x t -2, ···, respectively coefficients φ _{i (i} = 1 to _{x t-p,} ···, _p and those weighted by) may be represented by formula (a) as the assumed model and linear combination holds between the linear prediction error epsilon _t. For example, when any of the coefficients φ _i is 0, strictly speaking, the linear prediction model cannot be said to be based on the sample values of the past p time-series discrete signals, but including such a case, It is considered to be based on the past p time series discrete signal sample values. Here, p represents the number of samples required for the linear prediction model, and this is referred to as a model order.

  In all-pole linear prediction analysis, determination of the model order of the linear prediction model may be required. For example, taking compression coding as an example, it is necessary to determine the model order in order to minimize the sum of the code amounts of the auxiliary information related to the linear prediction coefficient and the prediction error waveform. The model order to be determined in this way is referred to as the optimal model order.

This will be described with reference to FIG. 7 as an example of calculating the PARCOR coefficient.
Each frame obtained by dividing a time discrete signal sampled at a predetermined time interval for each frame is set as an input signal y. Here, lossless coding [Lossless Coding] is taken as an example, and the input signal y is assumed to be an integer value obtained by integer conversion. The optimal model order is an n-order search range in which the model order is from N _min to N _max (where n is an integer that satisfies n ≧ 1). N _min and N _max are positive integers that satisfy N _max > N _min . ] Will be decided from those investigated. In the following, the model order m is a positive integer.

PARCOR coefficient calculation unit (901) is input with an input signal y, the model order is 1, 2, · · _·, PARCOR coefficient of each linear prediction model of _{N max} gamma _{m, m} [m = 1, 2, · · , N _max ] are calculated respectively [For the calculation method of the PARCOR coefficient, see Non-Patent Document 1 or << Calculation of PARCOR coefficient, forward prediction error sequence, backward prediction error sequence >> described later. ]. Model order is 1, _2, each PARCOR coefficient when the _{N max} gamma _{m, m} [m = 1,2, _{..., N max]} is an input PARCOR coefficient quantizer (902) Become.

The PARCOR coefficient quantization unit (902) quantizes each PARCOR coefficient γ _{m, m} [m = 1, 2,..., N _max ], and the model order is 1, 2 _,. Quantized PARCOR coefficients qγ _{m, m} [m = 1, 2,..., N _max ] are calculated respectively.
Further, the PARCOR coefficient quantization unit (902) uses the quantization table to calculate the quantized PARCOR coefficient for each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _max ]. scaled to a value of the index, these are encoded, to calculate coefficient code Cp _m [m = _1,2, ···, _{N max],} respectively.

Since the quantization method can use a known and well-known method, a detailed description thereof is omitted [for example, see Non-Patent Document 2]. ]. As described above, the quantized PARCOR coefficients qγ _{m, m} [m = 1, 2,..., N _max ] may be quantized, but some or all of them may be vector quantized together. May be. However, in the case of vector quantization, codes up to each model order included in the search range [that is, codes from model order 1 to N _min , codes from model order 1 to (N _min + n), models The orders are codes from 1 to (N _min + 2n),. ] Is set so that it can be selected by the optimal model order search / code selection unit (906), or each code is output to the optimal model order search / code selection unit (906). It takes a thing.
Each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _max ] is input to the linear prediction coefficient conversion unit (903).
Each coefficient code Cp _m [m = 1, 2,..., N _max ] is input to the optimum model order search / code selection unit (906).

The linear prediction coefficient conversion unit (903) calculates each n-th order of the model order from N _min to N _max from each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _max]. [Quantized] linear prediction coefficients γ _{m, k of} the linear prediction model according to the model order [search target model order m = N _min , N _min + n, N _min + 2n,..., N _min + n × i, ..., N _min + n × [(N _max −N _min ) / n], where [·] is a Gaussian symbol. As i _max = [(N _max −N _min ) / n], it is expressed as “i = 0, 1, 2,..., i _max ”. K = 1, 2,..., M. [Refer to Non-Patent Document 1 or << Calculation of PARCOR coefficient / forward prediction error sequence / backward prediction error sequence> described later for the calculation method of the linear prediction coefficient]. ]. That is, as i = 0, 1, 2,..., I _max , the linear prediction coefficient γ _{g, k} [k = 1, 2,..., Of the linear prediction model of the model order (N _min + n × i). g] was calculated [N _min + n × i = g, and the symbols were replaced. ]. Note that the linear prediction coefficients γ _{g, g} are already given as PARCOR coefficients.
The linear prediction coefficient γ _{g, k} [k = 1, 2,..., G] of the linear prediction model of model order (N _min + n × i) is a prediction error filter unit (904-i) [where i = 0, 1, 2,..., I _max ].

Each of the prediction error filter units (904-i) [i = 0, 1, 2,..., I _max ] receives an input signal y as an input, and linear prediction coefficients γ _{g, k} [k = 1, 2,. .., G] is passed through a prediction error filter having a prediction coefficient, for example, by passing the input signal y forward, and the error between the input signal y and the prediction value of the linear prediction model of model order g composed of linear prediction coefficients γ _{g, k} and outputs a certain prediction error signal [prediction error waveform] e _i.
The linear prediction error signal e _i is input to an error signal encoding unit (905-i) [i = 0, 1, 2,..., I _max ].

Each of the error signal encoding units (905-i) [i = 0, 1, 2,..., I _max ] encodes the prediction error signal e _i to generate an error of the model order (N _min + n × i). The signal code C _g [g = N _min + n × i] is output.
The error signal code C _g [g = N _min + n × i] is input to the optimum model order search / code selection unit (906).

The optimal model order search / code selection unit (906) performs error signal code C _g [g = N for model order (N _min + n × i) for i = 0, 1, 2,..., I _{max. min} + n × the amount of code i], the model order is _{(n min} + n × i) coefficients up code _{Cp m (m = 1,2, ···} , n min + n × i) the sum of the code amount of A certain total code amount is obtained, and a minimum total code amount is searched for among the obtained total code amounts. For example, when the code having the minimum total code amount is i = I, that is, when the model order is (N _min + n × I), the code amount and the model order of the error signal code C _g [g = N _min + n × I] are _{(n min} + n × I) to the coefficient code _{Cp m (m = 1,2, ···} , n min + n × I) the total code amount of the code amount of is assumed to be minimal, optimal model order is ( N _min + n × I).
Then, the communication path, coefficients corresponding to the PARCOR coefficient to the optimal model order _{(N min} + n × I) code _{Cp m (m = 1,2, ···} , N min + n × I) and optimal model order _{(n min} + n × I) error signal code _{C g} in the case of _{[g = n min} + n × I] is sent out. If the optimum model order cannot be determined from the coefficient code Cp _m (m = 1, 2,..., N _min + n × I), information representing the optimum model order [order information] is encoded as appropriate. The encoded order information is also sent to the communication path.

As in the above example, conventionally, the total code amount in each model order is obtained by brute force, and the smallest one is searched.
By Hino Mikio, “Spectral Analysis”, Asakura Shoten, 1979 Takehiro Moriya, “Speech coding”, The Institute of Electronics, Information and Communication Engineers, 1998

  For each model order, the auxiliary information related to the prediction coefficient and the prediction error waveform are encoded to obtain the total code amount, and the model order when the total code amount is minimized can be determined as the optimal model order. However, there is a problem that a lot of processing time is required.

  Therefore, in view of the above problems, the present invention provides a linear prediction model order determination apparatus, method, program, and recording medium that determine the optimal model order of a linear prediction model without actually obtaining the total code amount at each model order. The purpose is to provide.

In order to solve the above problems, the present invention is configured as follows. That is, from the input signal, the model order is 1,..., M [where m is an integer that satisfies 1 ≦ m ≦ N _sup for an integer N _{sup of} 2 or more. ] And the forward prediction error sequence and / or the backward prediction error sequence for the model order m, respectively. Then, from the forward prediction error sequence and / or the backward prediction error sequence at the model order m, the linearity of the model order m composed of linear prediction coefficients given by the PARCOR coefficients when the model order is 1,..., M. A code amount of a prediction error waveform that is an error between a prediction value of a prediction model and an input signal is estimated. Also, the code amount [individual code amount] corresponding to each PARCOR coefficient when the model order is 1,..., M, or the code amount [total code amount] corresponding to all PARCOR coefficients is calculated. For a plurality of model orders included in the model orders from 1 to _Nsup , the total code amount or the total of individual code amounts when the model order is 1,... The total [total code amount] with the estimated code amount of the waveform is obtained, and the model order is determined as one from the total code amount. The determined model order is the optimum model order.
In this way, instead of actually obtaining the code amount at each model order, the code amount of the prediction error waveform is estimated by the forward prediction error sequence and / or the backward prediction error sequence obtained in the PARCOR coefficient calculation process. It is configured.

  According to the present invention, since the code amount of the prediction error waveform is estimated from the forward prediction error sequence and / or the backward prediction error sequence obtained in the PARCOR coefficient calculation process, the code amount [coefficient code in each model order is actually calculated. The optimal model order of the linear prediction model can be determined without obtaining the total code amount of the amount and the code amount of the prediction error waveform].

[Technical explanation]
In the present invention, the optimal model order is determined by effectively using the prediction error sequence obtained in the PARCOR coefficient calculation process.

<< Calculation of PARCOR coefficient, forward prediction error sequence, backward prediction error sequence >>
A method for directly obtaining a PARCOR coefficient is known as one of all-pole linear prediction analysis. This calculation method is described as the Burg method in Non-Patent Document 1.

  An outline of the Burg method will be described. In the Burg method, it is considered that the linear prediction model of model order m−1 is extended to a linear prediction model of model order m. In other words, in the Burg method, the PARCOR coefficient of the linear prediction model is calculated from a recurrence relationship in which the PARCOR coefficient at a model order m that is one greater than the model order is calculated from the PARCOR coefficient at a certain model order m-1.

The given data _{x 1, x 2, ···,} and _{x N.} In this case, reference prediction error filter data (linear prediction model of model order _{m) x 1, x 2,} ···, average power _{P m} [wherein in the case through the case and backward forward pass _{x N} (1) . ] Is adopted on condition that it is minimized. However, γ _{m, k} (k = 1, 2,..., M) is a linear prediction coefficient of the model order m.

Here, γ _{m, k} (k = 1, 2,..., M−1) has a relationship represented by Expression (2), that is, Expression (3) [Levinson algorithm; see Non-Patent Document 1 above]. . ].

When the formula (1) is rewritten using the formula (2) or the formula (3), the formula (4) is obtained.

Here, b ′ _{m, i} is expressed by Equation (5). b ′ _{m, i} will be referred to as a forward prediction error sequence.
In addition, b _{m, i} is expressed by Equation (6). Let b _{m, i} be called a backward prediction error sequence.

Note that the forward prediction error sequence and the backward prediction error sequence have a relationship represented by Equations (7) and (8) [see Non-Patent Document 1 above]. ].

At this time, from the condition that the average output P _m is minimized, γ _{m, m} [so-called PARCOR coefficient. ], The formula (9) is obtained.

That is, the PARCOR coefficient γ _{m, m} is the inner product of the forward prediction error sequence b ′ _{m, i} (i = 1,..., N−m; the same applies hereinafter) and the backward prediction error sequence b _{m, i} [formula (9 ) Molecule. ] Of the respective energies of the forward prediction error sequence b ′ _{m, i} and the backward prediction error sequence b _{m, i} [hereinafter referred to as “average energy”]. This corresponds to the denominator of Equation (9). ] Divided by].
The linear prediction coefficient γ _{m, k} (k = 1, 2,..., M−1) when the model order is m is given by Equation (3). The linear prediction coefficient γ _{m, k} (k = m) is given by the equation (9) as a PARCOR coefficient.

Although the calculation of the PARCOR coefficient or the like based on the Burg method has been described above, the present invention is not limited to this method, and may be based on, for example, the Yule-Walker method. For the Yule-Walker method, see Non-Patent Document 1 above.
This is the end of the description of “Calculation of PARCOR coefficient, forward prediction error sequence, backward prediction error sequence”.

<< Information amount of PARCOR coefficient >>
Next, the information amount of the PARCOR coefficient will be described.
The calculation of the information amount of the PARCOR coefficient depends on the quantization method and the encoding method of the PARCOR coefficient. Therefore, here, a general method will be described as an example.
Generally, it is a data table in which a child element table [PARCOR coefficient and its quantized value correspond to each other. ] Is used to convert the PARCOR coefficient into an index value. Further, two examples are shown as encoding methods of the numerical values. The first method is to encode an index value with a predetermined number of bits including a sign bit (a bit representing positive / negative), and the coefficient code is a table of codes corresponding to the index value [index. Is a data table in which the numerical values of and the coefficient codes correspond to each other. ] Can be obtained with reference to. The second method is a case where a variable length code is used depending on the numerical value of the index of the PARCOR coefficient. In this case, since the number of bits varies depending on the absolute value of the index value of the PARCOR coefficient, the code table corresponding to the absolute value of the index value [the data table in which the absolute value of the index value corresponds to the coefficient code. ] To obtain the coefficient code. The code amount of the coefficient code obtained here corresponds to the information amount of the PARCOR coefficient. The table used for encoding is an encoding table [data table. I will say.

In addition, the quantization table and the coding table, which are separate data tables, are not used, but the range of the PARCOR coefficient and the combination of the quantization value and the code corresponding to the range are all the ranges that the PARCOR coefficient can take. Quantization / coding table [Data table. ] May be used.
For example, in the case of fixed-length coding, a quantization / coding table that is simply exemplified in Table 1 can be used. In the case of variable length coding, a quantization / coding table that is simply exemplified in Table 2 can be used. The codes shown in Table 2 are codes that can be instantaneously decoded with 0 as an index.

Two methods are conceivable for calculating the code amount of the coefficient code. The first method is to calculate all code amounts [total code amount] of coefficient codes corresponding to all PARCOR coefficients obtained for each model order from model order 1 to model order m. The second method is to calculate the code amount [individual code amount] of the coefficient code corresponding to the PARCOR coefficient at each model order for each model order from model order 1 to model order m. . In the second method, the sum of the individual code amounts of the respective coefficient codes is handled as the information amount of the PARCOR coefficient. This will also be described in <Determination of Optimal Model Order> described later.
This is the end of the description of “PARCOR coefficient information amount”.

<< Estimation of code amount of prediction error waveform >>
Next, estimation of the code amount of the prediction error waveform that is an error between the prediction value of the linear prediction model and the input signal will be described. The code amount of the prediction error waveform is estimated using an index of the average size of the sequence. Here, the series means any one of a forward prediction error series, a backward prediction error series, a series of both a forward prediction error and a backward prediction error. Further, an average size index [hereinafter simply referred to as “index”. ] Means the square root of the average of the energy of the series or the average of the absolute values of the series. This will be specifically described below.

First, a method of estimating the code amount of the prediction error waveform using the sequence energy will be described. Specifically, when the model order is m, an equation (from an average energy E that is an average of the energy of the forward prediction error sequence and the energy of the backward prediction error sequence of the frame [input signal] composed of N points of samples ( The index a represented by 10) is obtained.

Note that the forward prediction error sequence energy or the backward prediction error sequence energy of a frame composed of N samples may be used as the value E.
Alternatively, the index a may be given as an average of the index of the forward prediction error sequence and the index of the backward prediction error sequence. That is, assuming that the energy of the forward prediction error sequence is E _f , the index of the forward prediction error sequence is a _f , the energy of the backward prediction error sequence is E _r , and the index of the backward prediction error sequence is a _r , the index a is expressed by Equation (11). ).

For example, when a Golomb-Rice code as shown in Table 3 is used as a variable-length code, (twice the index + 1) is an approximation with a good number of code bits, so the estimated value of the prediction error waveform code amount [estimation] Code amount] S is obtained by Expression (12) assuming that 1 is added to twice the index a and multiplied by the number of samples of the input signal.

Further, the lower-order h bits of the prediction error series [where h is an integer of 1 or more. ] May be encoded as they are, and only the higher-order bits may be Golomb-Rice encoded. In this case, taking the case of the average energy E as an example, after the average energy E is shifted down by h bits, the index a ′ represented by the equation (13) is obtained. A value obtained by shifting the average energy E downward by h bits corresponds to a value obtained by multiplying the average energy E by 1/2 ^h , and Equation (10) corresponds to a case where h = 0.

In this case, the estimated value [estimated code amount] S ′ of the prediction error waveform in the frame is obtained by Expression (14) with the lower-order h bits added. Equation (12) corresponds to the case of h = 0.

Next, a method for estimating the code amount of the prediction error waveform using the absolute value of the prediction error sequence will be described. Specifically, the sum of the absolute values of the forward prediction error sequence and the absolute value of the backward prediction error sequence of a frame composed of N points of samples is obtained, and the sum D of the respective sums is expressed by Expression (15). The index a ₁ is obtained.

Similarly to the above, for example, a Golomb-Rice code as shown in Table 3 is used as a variable length code. At this time, the estimated value [estimated code amount] S ₁ of the prediction error waveform in the frame is obtained by Expression (16).

A method for estimating the code amount of a prediction error waveform using an absolute value of a prediction error sequence generally has high estimation accuracy. Note that the sum of the absolute values of the forward prediction error sequences or the sum of the absolute values of the backward prediction error sequences of a frame composed of N samples may be used as the value D.
Alternatively, the index a ₁ may be given as an average of the index of the forward prediction error sequence and the index of the backward prediction error sequence. That is, if the sum of the absolute values of the forward prediction error sequence is D _f1 , the index of the forward prediction error sequence is a _f1 , the sum of the absolute values of the backward prediction error sequence is D _r1 , and the index of the backward prediction error sequence is a _r1 . The index a ₁ is given by equation (17).
This is the end of the description of “Estimation of Code Amount of Prediction Error Waveform”.

<Determining the optimal model order>
The method for determining the optimal model order is not particularly limited, and three methods are exemplified here.
In the first method (method A), each model order m in the search range set in advance [not all model orders in the search range, but a part thereof, for example, each model order for each n may be used. ], The information amount of the PARCOR coefficient in the model order [that is, the coefficient code amount, but when the overall code amount is calculated as the coefficient code amount, the entire code amount is used as it is, and the individual code amount is used as the coefficient code amount. Is calculated, the code amount obtained by summing up the individual code amounts corresponding to all model orders of model orders 1 to m is handled as the information amount of the PARCOR coefficient. The same applies hereinafter. And the estimated code amount of the prediction error waveform in the model order [total code amount] is determined, and the model order when the total code amount is minimized is determined as the optimal model order.

  In the second method (Method B), the model order is sequentially set so as to be larger than the model order set immediately before, and the total code amount in the set model order is set to the previous one. When the total code amount in the model order is set, the model order set immediately before is determined as the optimum model order.

In the second method, when the total code amount in the set model order becomes larger than the total code amount in the previous model order, the model order set in the previous [provisional From the optimal order], a model order that is only a predetermined number (for example, a model order obtained by adding a positive integer F to the provisional optimal order). ], The total code amount [complementary total code amount] is obtained, and if there is a model order that has a complementary total code amount smaller than the total code amount in the provisional optimal order, the provisional optimal order It is also possible to change to a method in which the model order when the minimum complementary total code amount is smaller than the total code amount in the above is used as the optimal model order. This modification method (method C) basically uses the model order set immediately before the total code amount larger than the total code amount in the model order set immediately before [provisional The optimal code order] is a promising candidate for the optimal model order, and as a precaution, the increase or decrease in the total code amount is checked up to the model order ahead.
This completes the description of “Determining the optimal model order”.

[Embodiment]
Hereinafter, embodiments of the present invention will be described. In the present invention, the optimal model order of the linear prediction model is determined. The optimal model order may be determined as desired. For example, the optimal model order may be used for signal analysis or signal encoding. You may carry out as.
Here, from the viewpoint of clarifying the characteristics of the present invention from the comparison with the above-described prior art, the embodiment will be described by taking as an example the case where the present invention is used for signal coding.

<< First Embodiment >>
A first embodiment of the present invention will be described with reference to the drawings.
<Linear Prediction Model Order Determination Device of First Embodiment>
As illustrated in FIG. 1, the linear prediction model order determination device (1) includes an input unit (11) to which a keyboard or the like can be connected, an output unit (12) to which a liquid crystal display or the like can be connected, a CPU (Central Processing Unit; 14) [A cache memory or the like may be provided. RAM (Random Access Memory) (15), ROM (Read Only Memory) (16) and external storage device (17) which is a hard disk, and these input unit (11), output unit (12), A CPU (14), a RAM (15), a ROM (16), a bus (18) connected so as to exchange data between the external storage devices (17), and the like are provided. If necessary, the linear prediction model order determination device (1) may be provided with a device (drive) that can read and write a storage medium such as a CD-ROM. A physical entity having such hardware resources includes a general-purpose computer.

  The external storage device (17) of the linear prediction model order determination device (1) stores and stores a program for determining the optimal model order of the linear prediction model, data necessary for processing of this program, and the like. These are read into the memory in response to. Further, data obtained by the processing of these programs is appropriately stored and stored in a memory such as the RAM (15), and further read out as appropriate for use in information processing.

Each frame obtained by dividing a time discrete signal sampled at a predetermined time interval for each frame is set as an input signal x. Here an example of lossless coding [Lossless Coding], the input signal x comprises an integer converted integer value, x _1, x 2 as a data sequence number of samples _N, · · ·, when expressed as x _N To do. It is assumed that the input signal x is stored and stored as data in the external storage device (17). The optimal model order is an n-order search range in which the model order is from N _min to N _max (where n is an integer that satisfies n ≧ 1). N _min and N _max are positive integers that satisfy N _max > N _min . ] Will be decided from those investigated. In the following, the model order m is a positive integer. In general, if the upper limit of the model order is N _sup [the infinite model order is virtually impossible to investigate, the upper limit of the model order is determined. ], The search range can be set so that the model order is included in the range from 1 to _Nsup . That is, 1 ≦ N _min <N _max ≦ N _sup . However, in the case of N _max <N _sup , it is meaningless to obtain the PARCOR coefficient or the like for the model order from N _max +1 to N _sup, and therefore it is usually considered that N _max = N _sup . In this specification, N _max = N _sup .

  Signals handled in each embodiment are acoustic signals such as human voice, music, and noise / noise. Of course, the signal is not limited to an acoustic signal, and may be a biological signal, for example.

  The input signal x is not stored and stored in the external storage device (17) in advance. For example, the input signal x is read from a storage medium such as a CD-ROM and stored in the external storage device (17). Alternatively, in the case of a speech signal, it is assumed that the signal encoding device (2) including the linear prediction model order determining device (1) includes a microphone, and the collected sound signal obtained by the microphone is converted into an A / D converter / integer It can be considered that the input signal x is obtained by conversion. In any case, the component (means) necessary for executing A / D conversion and the like and the data providing technology from the recording medium are achieved by conventional means of the publicly known technology, and therefore the description and illustration are omitted. .

  The external storage device (17) stores and stores the above-described quantization / coding table (data table).

  In the external storage device (17), a program for calculating a PARCOR coefficient, a forward prediction error sequence, and a backward prediction error sequence, a program for estimating a code amount of a prediction error waveform, and a coefficient code amount are calculated. And a program for determining the optimum model order are stored and stored. In addition, a control program for controlling processing based on these programs may be stored as appropriate.

  In the linear prediction model order determining device (1), each program stored in the external storage device (17) and data necessary for processing each program are read into the RAM (15) as necessary, and the CPU (14 ) Is interpreted and executed. As a result, the CPU (14) realizes predetermined functions (PARCOR coefficient calculation unit, prediction error code amount estimation unit, coefficient code amount calculation unit, optimum model order search unit, control unit), and thereby the optimal model of the linear prediction model. Order determination is realized.

<Linear Prediction Model Order Determination Process of First Embodiment>
Next, with reference to FIG. 2 and FIG. 3, the flow of the optimal model order determination process in the linear prediction model order determination apparatus (1) will be described descriptively. In the first embodiment, the optimal model order is determined by the above method A.

First, the PARCOR coefficient calculation unit (141) receives the input signal x and inputs PARCOR coefficients γ _{m, m} [m = 1, 2, for each model order for each model order from 1 to N _max . , N _max ], forward prediction error sequence b ′ _{m, i} [m = 1, 2,..., N _max ], backward prediction error sequence b _{m, i} [m = 1, 2,. , N _max ] are calculated respectively (step S11). The calculation method of the PARCOR coefficient / forward prediction error sequence / backward prediction error sequence is as described in << Calculation of PARCOR coefficient / forward prediction error sequence / backward prediction error sequence >>, and Equations (7), (8), It can be determined using equation (9). Here, it should be noted that the subscript i does not indicate a specific value but corresponds to the index i in the equation (9) [the same applies hereinafter. The same applies to the subscript i in the figure. ]. That is, the number of data corresponding to the number of values that can be taken by the index i exists.
Each forward prediction error sequence b ′ _{m, i} [m = 1, 2,..., N _max ] and each backward prediction error sequence b _{m, i} [m = 1, 2 _,. This is an input to the prediction error code amount estimation unit (142). Each PARCOR coefficient γ _{m, m} [m = 1, 2,..., N _max ] is input to the coefficient code amount calculation unit (143).

Next, the prediction error code amount estimator (142) outputs each forward prediction error sequence b ′ _{m, i} [m = 1, 2,..., N _max ] and each backward prediction error sequence b _{m, i} [m = 1, 2,..., N _max ] is used to calculate an estimated value [estimated code amount] Ce _m [m = 1, 2,..., N _max ] of the prediction error waveform (step S12). The calculation of each estimated code amount is as described in << Estimation of code amount of prediction error waveform >>, and for each model order of m = 1, 2,..., N _max using any one method. To do.
Each estimated code amount Ce _m [m = 1, 2,..., N _max ] is input to the optimum model order search unit (144).

In addition, the coefficient code amount calculation unit (143) uses each PARCOR coefficient γ _{m, m} [m = 1, 2,..., N _max ] to use the coefficient code amount Cp _m [m = 1, 2,. .., N _max ] is calculated (step S13). The calculation of the coefficient code amount is as described in << Information amount of PARCOR coefficient >>, and is performed using any one method. In this embodiment, the individual code amount is obtained as an example.
Each coefficient code amount Cp _m [m = 1, 2,..., N _max ] is input to the optimum model order search unit (144).

  Either step S12 or step S13 may precede. That is, processing can be performed in the order of step S11, step S12, and step S13, and processing can also be performed in the order of step S11, step S13, and step S12.

Then, the optimal model order searching unit (144), each coefficient code amount Cp _m [m = _1,2, ···, _{N max]} and each estimated amount of code Ce _m [m = 1, 2, · · · , N _max ] determines the optimal model order of the linear prediction model. The process will be described.

First, the optimum model order search unit (144) calculates the total code amount in each model order for each model order from N _min to N _max that is the search range (step S14). That is, for each model order m [the search target model order] for each of n _min ≦ m ≦ N _max , the total of the individual code amounts up to the model order m, and the estimated code amount of the prediction error waveform at the model order m Find the sum of

Next, the optimum model order search unit (144) sets N _min to the parameter j for iterative processing, and sets an arbitrary value (initial value) to the parameter M for searching for the minimum total code amount (step S15). ). This initial value is set to a sufficiently large value.

Next, the optimum model order search unit (144) calculates the total code amount in the model order j (that is, the sum of the individual code amounts from the model orders 1 to j and the estimated code amount of the prediction error waveform in the model order j). Total. ] And M are determined (step S16). In the first embodiment, it is determined whether M> (total code amount in model order j) is satisfied.
If it is true that M> (total code amount in model order j) is true, optimal model order search unit (144) executes the process of step S17. If the establishment of M> (total code amount in model order j) is false, the optimal model order search unit (144) executes the process of step S18.

If it is true that M> (total code amount in model order j) is true, the optimum model order search unit (144) substitutes the value of parameter j for parameter N _opt for determining the optimum model order, and parameter M Is substituted with the total code amount in the model order j (step S17). Next, the process of step S18 is executed.

The optimum model order search unit (144) compares and determines the magnitude of the parameter j and the model order _Nmax (step S18). In the first embodiment, it is determined whether j ≧ N _max is satisfied.
If the establishment of j ≧ N _max is true, the optimum model order search unit (144) ends the optimum model order determination process. That is, the value of the parameter N _opt at this time is the optimal model order. The order information representing the optimum model order N _opt may be output from the signal encoding device (2). The optimal model order N _opt is an input to the PARCOR coefficient quantization unit (145).
If the establishment of j ≧ N _max is false, the optimum model order search unit (144) executes the process of step S19.

  The optimal model order search unit (144) sets n to the parameter j, that is, j + n as a new value of the parameter j (step S19), and performs the processing of steps S16 to S18.

Hereinafter, processing related to signal encoding after obtaining the most suitable model order will be described.
The PARCOR coefficient quantization unit (145) quantizes each PARCOR coefficient γ _{m, m} [m = 1, 2,..., N _opt ] obtained by the PARCOR coefficient calculation unit (141), and the model order is Quantized PARCOR coefficients qγ _{m, m} [m = 1, 2,..., N _opt ] are calculated for each model order from 1 to N _opt . Each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _opt ] is input to the linear prediction coefficient conversion unit (146).
Further, the PARCOR coefficient quantizing unit (145) uses the quantization table for each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _opt ] to quantize the PARCOR coefficient. Are converted into index numerical values, and these are encoded to calculate coefficient codes Cp _m [m = 1, 2,..., N _opt ], respectively. Each coefficient code Cp _m [m = 1, 2,..., N _opt ] is an output of the signal encoding device (2).

Next, the linear prediction coefficient conversion unit (146) calculates [quantized] from each quantized PARCOR coefficient qγ _{m, m} [m = 1, 2,..., N _opt ] according to the equation (3). ] Linear prediction coefficients γ _{g, k} [g = N _opt , k = 1, 2,..., G] are calculated. The [quantized] linear prediction coefficients γ _{g, k} [g = N _opt , k = 1, 2,..., G] are input to the prediction error filter unit (147).

Next, the prediction error filter unit (147) receives the input signal x as an input, and inputs the input signal to the prediction error filter using the linear prediction coefficients γ _{g, k} [k = 1, 2,..., G] as prediction coefficients. x is passed through a forward example, and outputs a prediction error signal [prediction error waveform] e _g is the error between the predicted values of the linear prediction model of model order g consisting of the input signal x and the linear prediction coefficient gamma _{g, k.}
Linear prediction error signal _{e g} becomes an input of the error signal encoding unit (148).

Next, the error signal encoding unit (148) is to encode the prediction error signal _{e g,} to output an error signal code _{C g [g = N opt].} This error signal code C _g [g = N _opt ] is the output of the signal encoding device (2).

  In the first embodiment, the coefficient code amount calculation unit (143) has been described as the case of calculating the individual code amount. When the coefficient code amount calculation unit (143) calculates the total code amount, in the process of step S16, the optimum model order search unit (144) determines the total code amount in the model order j [that is, the total code amount and , And the estimated code amount of the prediction error waveform at the model order j. ] And M may be determined.

<< Second Embodiment >>
A second embodiment of the present invention will be described with reference to the drawings.
Since the hardware configuration and program of the linear prediction model order determination device of the second embodiment are the same as those of the first embodiment, description thereof will be omitted.
<Linear Prediction Model Order Determination Process of Second Embodiment>
With reference to FIG. 2 and FIG. 4, the flow of the optimal model order determination processing in the linear prediction model order determination apparatus (1) of the second embodiment will be described descriptively. In the second embodiment, the optimum model order is determined by the method B. Note that << calculation of PARCOR coefficient / forward prediction error sequence / backward prediction error series >>, << estimation of code amount of prediction error waveform >>, and << information amount of PARCOR coefficient >> are as described above, and thus description thereof is omitted. [See [Technical Description] and << First Embodiment >>. ].

First, the control unit (190) sets N _min to the parameter j (step S21).

Next, the PARCOR coefficient calculation unit (141) receives the input signal x and inputs PARCOR coefficients γ _{m, m} [m = 1, 2, for each model order for each model order from 1 to j. , J], forward prediction error sequence b ′ _{m, i} [m = 1, 2,..., J], backward prediction error sequence b _{m, i} [m = 1, 2,. ] Are calculated respectively (step S22).
Each forward prediction error series b ′ _{m, i} [m = 1, 2,..., J] and each backward prediction error series b _{m, i} [m = 1, 2,. This is an input to the code amount estimation unit (142). Each PARCOR coefficient γ _{m, m} [m = 1, 2,..., J] is input to the coefficient code amount calculation unit (143).

Next, the prediction error code amount estimation unit (142) outputs each forward prediction error sequence b ′ _{m, i} [m = 1, 2,..., J] and each backward prediction error sequence b _{m, i} [m = 1. , 2,..., J] is used to calculate the estimated value [estimated code amount] Ce _m [m = j] of the prediction error waveform (step S23).
Each estimated code amount Ce _m [m = j] is input to the optimum model order search unit (144).

In addition, the coefficient code amount calculation unit (143) uses each PARCOR coefficient γ _{m, m} [m = 1, 2,..., J] and uses the coefficient code amount Cp _m [m = 1, 2,..., J] are calculated (step S24). Each coefficient code amount Cp _m [m = 1, 2,..., J] is input to the optimum model order search unit (144).

  Either step S23 or step S24 may precede. That is, it can also process in the order of step S21, step S22, step S23, and step S24, and can also process in the order of step S21, step S22, step S24, and step S23.

Next, the optimal model order search unit (144) sequentially sets the coefficient code amount Cp _m [m = 1, 2,..., J] and the estimated code amount Ce _m [m = j] of the model order m. ] Is used to determine the optimal model order of the linear prediction model. The process will be described.

  First, the optimal model order search unit (144) calculates the total code amount in the model order j (step S25). That is, the sum of the individual code amounts from model orders 1 to j and the estimated code amount of the prediction error waveform at model order j is obtained.

Next, the optimal model order search unit (144) determines whether or not the parameter j is equal to N _min (step S26).
If the parameter j is equal to N _min , the optimal model order search unit (144) executes the process of step S29. If the parameter j is not equal to N _min , the optimal model order search unit (144) executes the process of step S27.

Since j = N _min at this stage, the process of step S29 is performed.
The optimum model order search unit (144) sets the total code amount in the model order j as the parameter M (step S29). Next, the control unit (190) sets the parameter j obtained by adding n, that is, j + n to a new value of the parameter j (step S30), and performs control so as to perform the processing of steps S22 to S26.

After this stage, since it is not determined that the parameter j is equal to N _min in the process of step S26, the process of step S27 is performed.
The optimal model order search unit (144) is the total code amount in the model order j [that is, the sum of the individual code amounts from model orders 1 to j and the estimated code amount of the prediction error waveform in model order j. . ] And M are determined (step S27). In the second embodiment, whether or not M> (total code amount in model order j) is determined as in the first embodiment.
If it is true that M> (total code amount in model order j) is true, optimal model order search unit (144) executes the process of step S28. If the establishment of M> (total code amount in model order j) is false, the optimum model order search unit (144) executes the process of step S32.

If the establishment of M> (total code amount in model order j) is false, the optimum model order search unit (144) substitutes the value of j−n for the optimum model order N _opt (step S32). Then, the optimum model order search unit (144) ends the optimum model order determination process. The order information representing the optimum model order N _opt may be output from the signal encoding device (2). Further, the optimal model order N _opt can be input to the PARCOR coefficient quantization unit (145).

If M> (total code amount in model order j) is true, the optimum model order search unit (144) compares and determines the magnitude of the parameter j and the model order _Nmax (step S28). In the second embodiment, as in the first embodiment, it is determined whether j ≧ N _max is satisfied.
If the establishment of j ≧ N _max is true, the optimum model order search unit (144) substitutes the value of the parameter j for the optimum model order N _opt (step S31). Then, the optimum model order search unit (144) ends the optimum model order determination process. The order information representing the optimum model order N _opt may be output from the signal encoding device (2). Further, the optimal model order N _opt can be input to the PARCOR coefficient quantization unit (145).
If the establishment of j ≧ N _max is false, the optimum model order search unit (144) executes the process of step S29. The processing after step S29 is as described above.

  In the second embodiment, the case where the coefficient code amount calculation unit (143) calculates the individual code amount has been described. When the coefficient code amount calculation unit (143) calculates the total code amount, in the process of step S27, the optimal model order search unit (144) determines the total code amount and the estimated code of the prediction error waveform at the model order j. What is necessary is just to calculate | require the total with quantity. In the process of step S27, the optimum model order search unit (144) calculates the total code amount in the model order j (that is, the sum of the total code amount and the estimated code amount of the prediction error waveform in the model order j). . ] And M may be determined. The same applies to the third embodiment described later.

  For example, when processing related to signal encoding after obtaining the highest model order is performed, the description thereof is omitted because it is as described in the first embodiment.

<< Third Embodiment >>
A third embodiment of the present invention will be described with reference to the drawings.
Since the hardware configuration and program of the linear prediction model order determination device of the third embodiment are the same as those of the first embodiment and the second embodiment, description thereof will be omitted.
<Linear Prediction Model Order Determination Processing of Third Embodiment>
With reference to FIG. 2, FIG. 5, and FIG. 6, the flow of the optimal model order determination process in the linear prediction model order determination apparatus (1) of the third embodiment will be described descriptively. In the third embodiment, the optimal model order is determined by the above method C.
Since the outline of the processing is the same as that of the second embodiment, only parts different from those of the second embodiment will be described here.

First, the control unit (190) sets N _min to the parameter j and 1 to the parameter f (step S21a). Subsequent to step S21a, the processing after step S22 described in the second embodiment is performed.

  In the second embodiment, if the establishment of M> (total code amount in model order j) is false in the process of step S27, the optimal model order search unit (144) executes the process of step S32. In 3 embodiment, the process after step S32a mentioned later is performed.

The optimal model order search unit (144) substitutes the value of j−n for the parameter j _temp (step S32a).

Next, the optimum model order search unit (144) has a parameter f and a constant F [the same as the positive integer F described above, and is stored and stored in the memory in advance. ] Is compared and determined (step S32b). In the second embodiment, it is determined whether f ≧ F is satisfied.
At this stage, establishment of f ≧ F is false, and the process of step S33 is executed.

  The optimum model order search unit (144) sets a value obtained by adding 1 to the parameter f, that is, f + 1 to a new value of the parameter f (step S33).

  Subsequently, the optimum model order search unit (144) sets the parameter j plus 1, that is, j + 1 as a new value of the parameter j (step S34).

  And a control part (190) is controlled to perform the process of step S35-S39. The process of step S35 is the process of step S22, the process of step S36 is the process of step S23, the process of step S37 is the process of step S24, the process of step S38 is the process of step S25, and the process of step S39 is the step. Since it is the same as the process of S27, explanation is omitted.

If M> (total code amount in model order j) is true in the process of step S39, the optimum model order search unit (144) executes the process of step S40. Since the process of step S40 is the same as the process of step S29, description is abbreviate | omitted. Following the process of step S40, the process of step S41 is performed. That is, the optimal model order search unit (144) substitutes the value of the parameter j for the parameter j _temp (step S41).
In the processing in step S39, M> if the establishment of (total amount of codes in the model order j) is false, the optimal model order searching unit (144) performs the process of step S32 b.

In the processing of step S32 b, until the establishment of f ≧ F is true, the process of step S33~S41 are performed.
In the processing of step S32 b, if the establishment of f ≧ F is true, the optimal model order searching unit (144) substitutes the value of the parameter _{j temp} optimal model order _{N opt} (step S 42). Then, the optimum model order search unit (144) ends the optimum model order determination process. The order information representing the optimum model order N _opt may be output from the signal encoding device (2). Further, the optimal model order N _opt can be input to the PARCOR coefficient quantization unit (145).

  For example, when processing related to signal encoding after obtaining the highest order model order is performed as described in the first embodiment, description thereof is omitted.

  In addition to the above embodiments, the linear prediction model order determination apparatus and method according to the present invention are not limited to the above-described embodiments, and can be appropriately changed without departing from the spirit of the present invention. In addition, the processing described in the linear prediction model order determination device / method is not only executed in time series according to the order of description, but also in parallel or individually according to the processing capability of the device that executes the processing or as necessary. It may be executed.

  When the processing function in the linear prediction model order determination device is realized by a computer, the processing contents of the function that the linear prediction model order determination device should have are described by a program. By executing this program on a computer, the processing function in the linear prediction model order determination device is realized on the computer.

  The program describing the processing contents can be recorded on a computer-readable recording medium. As the computer-readable recording medium, for example, any recording medium such as a magnetic recording device, an optical disk, a magneto-optical recording medium, and a semiconductor memory may be used. Specifically, for example, as a magnetic recording device, a hard disk device, a flexible disk, a magnetic tape or the like, and as an optical disk, a DVD (Digital Versatile Disc), a DVD-RAM (Random Access Memory), a CD-ROM (Compact Disc Read Only). Memory), CD-R (Recordable) / RW (ReWritable), etc., magneto-optical recording medium, MO (Magneto-Optical disc), etc., semiconductor memory, EEP-ROM (Electronically Erasable and Programmable-Read Only Memory), etc. Can be used.

  The program is distributed by selling, transferring, or lending a portable recording medium such as a DVD or CD-ROM in which the program is recorded. Furthermore, the program may be distributed by storing the program in a storage device of the server computer and transferring the program from the server computer to another computer via a network.

  A computer that executes such a program first stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage device. When executing the process, the computer reads a program stored in its own recording medium and executes a process according to the read program. As another execution form of the program, the computer may directly read the program from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to the computer. Each time, the processing according to the received program may be executed sequentially. Also, the program is not transferred from the server computer to the computer, and the above-described processing is executed by a so-called ASP (Application Service Provider) type service that realizes the processing function only by the execution instruction and result acquisition. It is good. Note that the program in this embodiment includes information that is used for processing by an electronic computer and that conforms to the program (data that is not a direct command to the computer but has a property that defines the processing of the computer).

  In this embodiment, the linear prediction model order determination device is configured by executing a predetermined program on a computer. However, at least a part of these processing contents may be realized by hardware. Good.

  The present invention is useful for signal analysis and signal encoding using an all-pole linear prediction model.

The figure which shows the hardware structural example of the linear prediction model order determination apparatus concerning 1st Embodiment. The block diagram which shows the function structural example of a linear prediction model order determination apparatus. The figure which shows the processing flow of the linear prediction model order determination process concerning 1st Embodiment. The figure which shows the processing flow of the linear prediction model order determination process concerning 2nd Embodiment. The figure which shows the processing flow of the linear prediction model order determination process concerning 3rd Embodiment. The figure which shows the processing flow of the linear prediction model order determination process concerning 3rd Embodiment (it is a figure attached to FIG. 5). The block diagram which shows the function structural example of the conventional linear prediction model order determination apparatus.

Explanation of symbols

1 linear prediction model order determination device 141 prediction error code amount estimation unit 142 coefficient code amount calculation unit 143 optimal model order search unit 145 PARCOR coefficient quantization unit 146 linear prediction coefficient conversion unit 147 prediction error filter unit 148 error signal encoding unit 190 Control unit

Claims (20)

The model order, which is the number of samples required for the linear prediction model, is 1,..., M from the input signal, where m is an integer that satisfies 1 ≦ m ≦ N _sup for an integer N _{sup of} 2 or more. PARCOR coefficient calculating means for calculating the PARCOR coefficient of each linear prediction model and the forward prediction error sequence and / or the backward prediction error sequence when the model order is m,
From the forward prediction error sequence when the model order is m and / or the backward prediction error sequence, the model order m consisting of linear prediction coefficients given by the PARCOR coefficients when the model order is 1,. A prediction error code amount estimation means for estimating a code amount of a prediction error waveform which is an error between a prediction value of a linear prediction model and an input signal;
Coefficient code amount calculation for calculating a code amount [individual code amount] corresponding to each PARCOR coefficient when the model order is 1,..., M, or a code amount [total code amount] corresponding to all PARCOR coefficients. Means,
For a plurality of model orders included in model orders from 1 to N _sup , the total code amount or the total of the individual code amounts when the model order is 1,. Linear prediction model order determination comprising: an optimal model order search means for obtaining a total [total code quantity] with the code quantity estimated by the prediction error code quantity estimation means and determining one model order from the total code quantity apparatus. The prediction error code amount estimation means includes:
An index value of an average size of the forward prediction error sequence when the model order is m or the backward prediction error sequence when the model order is m;
Alternatively, an index value of an average size of both the forward prediction error sequence and the backward prediction error sequence when the model order is m,
Alternatively, the average of the index value of the average magnitude of the forward prediction error sequence when the model order is m and the index value of the average magnitude of the backward prediction error sequence when the model order is m,
Is multiplied by ½ ^h [where h is an integer of 0 or more. The linear prediction model order determination apparatus according to claim 1, wherein the code amount of the prediction error waveform is estimated using the above. The prediction error code amount estimation means includes :
Motor multiplied by the number of samples of the input signal to the sum of 1 + h twice above the forward prediction error sequence or when the model order m of average size indicator value of the backward prediction error sequence when Dell order m thing,
Alternatively, the value obtained by adding 1 + h to twice the index value of the average size of both the forward prediction error sequence and the backward prediction error sequence when the model order is m, and multiplying by the number of samples of the input signal ,
Alternatively, 1 + h is set to twice the average of the index value of the average magnitude of the forward prediction error sequence at the model order m and the index value of the average magnitude of the backward prediction error sequence at the model order m. Multiply the sum by the number of samples of the input signal,
The linear prediction model order determination apparatus according to claim 2, wherein any one of the above is used as an estimated code amount of the prediction error waveform. The index value of the average size is
The linear prediction model order determination apparatus according to claim 2 or 3, wherein the linear prediction model order determination device is an average of the absolute values of the series or a square root of an average of the energy of the series. The coefficient code amount calculation means includes:
The linear prediction model order determination apparatus according to any one of claims 1 to 4, wherein the individual code amount or the entire code amount is obtained by a code amount of a fixed-length code that does not depend on a value of a PARCOR coefficient. The coefficient code amount calculation means includes:
The linear prediction model order determination apparatus according to any one of claims 1 to 4, wherein the individual code amount or the entire code amount is obtained by a code amount of a variable-length code depending on a value of a PARCOR coefficient. The optimum model order search means includes:
For a plurality of model orders included in model orders from 1 to N _sup , the total code amount or the total of the individual code amounts when the model order is 1,. The total code amount with the code amount estimated by the prediction error code amount estimation means is obtained, and the model order when the total code amount is minimized is determined as the optimum model order. The linear prediction model order determination apparatus according to any one of the above. Until the model order is determined by the optimum model order search means, the model order is sequentially set so that the model order is larger than the previous model order, and the set model is set for each set model order. A control means for executing the PARCOR coefficient calculating means, the prediction error code amount estimating means, the coefficient code amount calculating means, and the optimum model order searching means for the order m;
The optimum model order search means whose execution is controlled by the control means is:
In the model order m set by the control means, the total code amount or the sum of the individual code amounts when the model order is 1,..., M, and the prediction error code amount estimation means The total code amount with the code amount is obtained, and when the total code amount becomes larger than the total code amount in the model order M set immediately before the model order m, the model order M is determined as the optimum model order. The linear prediction model order determination apparatus according to any one of claims 1 to 6, wherein Until the model order is determined by the optimum model order search means, the model order is sequentially set so that the model order is larger than the previous model order, and the set model is set for each set model order. A control means for executing the PARCOR coefficient calculating means, the prediction error code amount estimating means, the coefficient code amount calculating means, and the optimum model order searching means for the order m;
The optimum model order search means whose execution is controlled by the control means is:
In the model order m set by the control means, the total code amount or the sum of the individual code amounts when the model order is 1,..., M, and the prediction error code amount estimation means The total code amount with the code amount is obtained, and from the model order M [provisional optimum order] when the total code amount is larger than the total code amount in the model order M set immediately before the model order m. When there is a model order having a complementary total code amount that is smaller than the total code amount in the provisional optimal order for the total code amount [complementary total code amount] in each model order up to a predetermined previous model order In this case, the model order when the minimum complementary total code amount of the complementary total code amounts is determined as the optimal model order, and the complementary total code amount is smaller than the total code amount at the provisional optimal order. If the Dell order is not present, the linear prediction model order determination apparatus according to any one of claims 1 to 6 is intended to determine the temporary optimal order as the optimal model order. From the input signal, the model order is a number of samples required for linear prediction model is 1, · · ·, m [where, m is an integer satisfying 1 ≦ m ≦ _{N sup} for two or more integer _{N sup.} A PARCOR coefficient of each linear prediction model and a forward prediction error sequence and / or a backward prediction error sequence when the model order is m, respectively,
From the forward prediction error series and / or said backward prediction error sequence when the model order m, the model order m that the model order is 1, ..., consisting of the linear prediction coefficients given by the respective PARCOR coefficient when the m A prediction error code amount estimation step for estimating a code amount of a prediction error waveform which is an error between a prediction value of the linear prediction model and an input signal;
Model order is 1, ..., coefficient coding amount for calculating a code amount corresponding to the respective PARCOR coefficient [individual code amount], or the code amount corresponding to all the PARCOR coefficient [total code amount] when m A calculation step;
For a plurality of model orders included in model orders from 1 to N _sup , the total code amount or the total of the individual code amounts when the model order is 1,. the prediction at the error code amount estimation step obtains a sum [total code amount] between the estimated code amount, the linear prediction model order and a optimal model order search step of determining model order one from the total code amount Decision method. The prediction error code amount estimation step includes:
An index value of an average size of the forward prediction error sequence when the model order is m or the backward prediction error sequence when the model order is m;
Alternatively, an index value of an average size of both the forward prediction error sequence and the backward prediction error sequence when the model order is m,
Alternatively, the average of the index value of the average magnitude of the forward prediction error sequence when the model order is m and the index value of the average magnitude of the backward prediction error sequence when the model order is m,
Is multiplied by ½ ^h [where h is an integer of 0 or more. The linear prediction model order determination method according to claim 10, wherein the code amount of the prediction error waveform is estimated using the above. The prediction error code amount estimation step includes :
Motor multiplied by the number of samples of the input signal to the sum of 1 + h twice above the forward prediction error sequence or when the model order m of average size indicator value of the backward prediction error sequence when Dell order m thing,
Alternatively, the value obtained by adding 1 + h to twice the index value of the average size of both the forward prediction error sequence and the backward prediction error sequence when the model order is m, and multiplying by the number of samples of the input signal ,
Alternatively, 1 + h is set to twice the average of the index value of the average magnitude of the forward prediction error sequence at the model order m and the index value of the average magnitude of the backward prediction error sequence at the model order m. Multiply the sum by the number of samples of the input signal,
The linear prediction model order determination method according to claim 11, wherein any one of the above is used as an estimated code amount of the prediction error waveform. The index value of the average size is
The linear prediction model order determination method according to claim 11 or 12, which is the average of the absolute values of the series or the square root of the average of the energy of the series. The coefficient code amount calculation step includes:
The linear prediction model order determination method according to any one of claims 10 to 13, wherein the individual code amount or the entire code amount is obtained by a code amount of a fixed-length code that does not depend on a value of a PARCOR coefficient. The coefficient code amount calculation step includes:
The linear prediction model order determination method according to any one of claims 10 to 13, wherein the individual code amount or the entire code amount is obtained by a code amount of a variable length code depending on a value of a PARCOR coefficient. The optimal model order search step includes
For a plurality of model orders included in model orders from 1 to N _sup , the total code amount or the total of the individual code amounts when the model order is 1,. claim from the prediction error code amount at the estimation step seeking total code amount of the estimated code amount claim 10 total code amount is to determine the model order of when a minimum as the optimum model order The linear prediction model order determination method according to any one of 15. Until the model order is determined in optimal model order search step, the model order is to set the model order to sequentially so larger than the model order of the previous one, the model orders are set, it is set For the model order m, the PARCOR coefficient calculation step, the prediction error code amount estimation step, the coefficient code amount calculation step, a control step for executing each process of the optimal model order search step,
The optimal model order search step whose execution is controlled in the control step is:
In model order m set in the control step, the total code amount or model order is 1, ..., the the sum of the individual code amount when the m, estimated in the above-mentioned prediction error code amount estimation step When the total code amount is larger than the total code amount in the model order M set immediately before the model order m, the model order M is optimized. The linear prediction model order determination method according to any one of claims 10 to 15, which is determined as a model order. Until the model order is determined in optimal model order search step, the model order is to set the model order to sequentially so larger than the model order of the previous one, the model orders are set, it is set For the model order m, the PARCOR coefficient calculation step, the prediction error code amount estimation step, the coefficient code amount calculation step, a control step for executing each process of the optimal model order search step,
The optimal model order search step whose execution is controlled in the control step is:
In model order m set in the control step, the total code amount or model order is 1, ..., the the sum of the individual code amount when the m, estimated in the above-mentioned prediction error code amount estimation step The total code amount with the calculated code amount is obtained, and the model order M [provisional optimal order when the total code amount becomes larger than the total code amount in the model order M set immediately before the model order m. ] For the total code amount (complementary total code amount) in each model order up to a predetermined previous model order, there is a model order that has a complementary total code amount smaller than the total code amount in the provisional optimal order. When determining, the model order when the minimum complementary total code amount of the complementary total code amount is determined as the optimal model order, and the complementary total code smaller than the total code amount in the provisional optimal order And when the model order is not present comprising linear prediction model order determination method according to any one of claims 15 claim 10 in which determining a provisional optimum order as the optimal model order. Program for functioning as a linear predictive model order determination apparatus according to the computer of claims 1 to claim 9.       A computer-readable recording medium on which the program according to claim 19 is recorded.

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